Deep Neural Network Model for Proficient Crop Yield Prediction

• Published in: E3S Web of Conferences Vol. 309; p. 1031
• Main Authors: Pravallika, K., Karuna, G., Anuradha, K., Srilakshmi, V.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 2021
• Subjects: Agricultural production; Agricultural research; Agriculture; Algorithms; Artificial neural networks; Cereal crops; Climate change; Climatic conditions; Cotton; Crop production; Crop yield; Crops; Datasets; Deep learning; Economic forecasting; Farmers; Forecasting; International trade; Learning algorithms; Machine learning; Neural networks; Predictions; Soil conditions; Soil types; Sugarcane; Tea
• ISSN: 2267-1242; 2555-0403; 2267-1242
• DOI: 10.1051/e3sconf/202130901031

Crop yield forecasting mainly focus on the domain of agriculture research which has a great impact on making decisions like import-export, pricing and distribution of respective crops. Accurate predictions with well timed forecasts is very important and is a tremendously challenging task due to numerous complex factors. Mainly crops like wheat, rice, peas, pulses, sugarcane, tea, cotton, green houses etc. can be used for crop yield prediction. Climatic changes and unpredictability influence mainly on crop production and maintenance. Forecasting crop yield well before harvest time can help farmers for selling and storage. Agriculture deals with large datasets and knowledge process. Many techniques are there to predict the crop yield. Farmers are benefited commercially by these predictions. Factors such as Geno type, Environment, Climatic conditions and Soil types used in predicting the Yield. For predicting accurately we need to know the fundamental understanding and relationship between the interactive factors and the yield to reveal the relationships between the datasets which are comprehensive and powerful algorithms. Based on the study of various survey papers it has been found that in all the crop predictions, various deep learning, machine learning and ANN algorithms implemented to predict yield forecast and the results are analyzed.